Regional Climate Model Emulation with Diffusion Approaches: What is the Added Value of Generative Machine Learning?
Emulators provide a cost-effective alternative to regional climate models (RCMs) by capturing their dynamical downscaling function. They link large-scale predictors simulated by global climate models (GCMs) to RCM-simulated high-resolution fields of the target variable, here precipitation. Machine learning methods, typically deep learning, are cheaper than running RCMs in computation time and energy. Among them, generative models are appealing because they can simulate ensembles of local high-resolution fields consistent with the predictors. This ensemble, which we call the uncertainty envelope, remains to be properly assessed for added value. Here, we make three contributions. First, we ...
| Authors | Mikel N. Legasa, Antoine Doury, Achille Gellens et al. |
|---|---|
| Primary category | physics.ao-ph |
| Published | 12 June 2026 |
| Last updated at source | 12 June 2026 |
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